As the business world has grown in the 21st century, artificial intelligence (AI) has grown with it, to the point that it is no longer a rare occurrence or unique application. Companies are using AI in several different ways, among them improving customer relations, creating more efficient processes, and making smarter business decisions. Cross-enterprise AI has changed the rules regarding the way executives can tailor product, marketing and sales strategies. There are several different technologies underneath the broader umbrella of artificial intelligence; business analytics, data science, data engineering and machine learning.

Business analytics is a wide, encompassing term that simply refers to the various ways in which we use data to derive meaningful patterns and draw conclusions. We use this data to tell us why things happened in the past and hopefully why they will happen in the future. It’s a process that has been used for ages, as business attempt to gain an upper hand, and can be as simple or as complicated as you like. But the field of business analytics is constantly changing, evolving as better techniques become available and stronger improvements are made every day.

Data science is what allows business analytics to continue to improve. As the name implies, it is a scientific process focused on the study of data. Data scientists create new analytic processes in search of the best ways to obtain insights and understanding into the markets they serve. Companies who wish to stay ahead of the curve in artificial intelligence should ensure they have several data scientists staffed in order to push the boundaries of their analytic capabilities further than their competitors.

Data engineering is what allows data analytics and data sciences to be as productive as they are. It makes data usable for the analytics and sciences to work with. Data engineers convert masses of data that have been collected in systems and data silos into groups of useful data which can be put into applications and through algorithms in order to find the meaning you want. As more and more data become available – and the amount of data being extracted across the world grows every day – data engineering becomes more important if we want to sift through the excess and get to the data points that mean the most to us.

Machine learning is a more of a field contained within these different types of artificial intelligence in which computers don’t just use data to spit out results, but also to learn from the data and use it to improve its own systems. The machine is given a set of rules and then is essentially given large quantities of data and set free. This version of AI does not require the structured data that analytics or data science does, and thus can find results from datasets that might otherwise be unusable.

So, with this knowledge in hand, what might be the best ways to implement AI into your own enterprise? Almost every organization knows AI can help them, but they aren’t sure how. Determining the right kinds of analytics your company needs is essential. What kind of problems do you need to solve? What needs to improve? Is the proper organizational structure in place to take fullest advantage of this technology? Knowing why and how you want to utilize cross-enterprise AI is a vital first step.

You next must ask how you plan to implement cross-enterprise AI, which generally offers two choices: buying an application that is already prepared or building your own to suit your needs. Both paths offer pros and cons, which is why deciding what is right for your company is even more important.

Building your own AI can also make sense if your enterprise requires a more tailored solution not typical of cross-enterprise AI. Whether a niche industry or unique business process, most cloud vendors may not offer the application you need. Luckily, investing in data scientists can help you develop your own AI applications, and many cloud platforms, even if they can’t provide a ready-made solution, do have tools that assist with some of the most daunting obstacles in creating your own artificial intelligence.

Buying already-built cross-enterprise artificial intelligence, however, provides a much easier entry into the world of AI. Costs are lower and implementation time is shorter; there won’t be any need for development platforms or solutions in buying or integrating. Because it’s much easier to install, you will also see benefits from your investment sooner. This path is best for a company who has a very defined need that is common within the business world, and which wants to use AI in very common use cases such as Finance, Marketing, Operations and Sales, where the AI systems for these use cases almost certainly already exist. For these types of uses, which you had to build say 5-10 years ago, building your own today would be akin to those who started building the own ERP system in the mid-90’s. You generally will get about 80% out of the box with buy solution, the remaining 20% being the customization to your business and specific use cases.